2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)最新文献_第7页

A Distributed Genetic Algorithm with Adaptive Diversity Maintenance for Ordered Problems 有序问题的自适应多样性保持分布式遗传算法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00063

Ryoma J. Ohira, Md. Saiful Islam

引用次数: 2

Development of Semantic Model of Multi-Level-Building Navigation Using Indoor Ontology and Dijkstra's Algorithm 基于室内本体和Dijkstra算法的多层建筑导航语义模型开发

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00100

Ye Yuan, Xiaoying Kong, Gengfa Fang, Li Liu, Sanya Khruahong

引用次数: 1

A Holistic Stream Partitioning Algorithm for Distributed Stream Processing Systems 分布式流处理系统的整体流划分算法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00046

Kejian Li, Gang Liu, Minhua Lu

{"title":"A Holistic Stream Partitioning Algorithm for Distributed Stream Processing Systems","authors":"Kejian Li, Gang Liu, Minhua Lu","doi":"10.1109/PDCAT46702.2019.00046","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00046","url":null,"abstract":"The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116364581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Reachability in Multithreaded Programs Is Polynomial in the Number of Threads 多线程程序的可达性是线程数的多项式

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00078

A. Malkis

引用次数: 0

Message from the General Chairs 主席致辞

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/pdcat46702.2019.00005

D. D’Auria, Masahiro Hayakawa, Sheida Malekpour, Stephan Matzka, M. Moreno, Aleksander Slominski, Atsushi Kitazawa

引用次数: 0

Joint Mobile Data Collection and Energy Supply Scheme for Rechargeable Wireless Sensor Networks 可充电无线传感器网络的联合移动数据收集和能源供应计划

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00098

Zhansheng Chen, Hong Shen

引用次数: 3

Optimising Deep Learning Split Deployment for IoT Edge Networks 优化物联网边缘网络的深度学习分离部署

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00069

Cailen Robertson, Jia Li, Ryoma J. Ohira, Quoc Viet Hung Nguyen, Jun Jo

{"title":"Optimising Deep Learning Split Deployment for IoT Edge Networks","authors":"Cailen Robertson, Jia Li, Ryoma J. Ohira, Quoc Viet Hung Nguyen, Jun Jo","doi":"10.1109/PDCAT46702.2019.00069","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00069","url":null,"abstract":"The Internet of Things (IoT) often generates large volumes of messy data which are difficult to process efficiently. While deep learning models have demonstrated their suitability in processing this data, the memory and processing requirements makes it difficult to deploy on edge nodes while achieving viable throughput results. Current solutions involve deploying the model in the cloud, but this leads to increased network costs due to the transfer of raw data. However, the layer based design of deep learning models allows for a model to be split into sub-models and deployed separately across IoT nodes. By deploying parts of the model on the edge node and in the cloud, the edge node is able to transmit an intermediate layer's feature output to the following sub-model instead of the raw input data. This reduces the size of the data being transmitted and results in a lower cost to the network. However, selecting the best layer to split the model becomes a multi-objective optimisation problem. In this paper, we propose an optimisation method that considers the network cost, input rate and processing overhead in selecting the best layer for splitting a model across an IoT network. We profile several popular model architectures to highlight their performance using this split deployment. Results from simulated and physical tests of the optimal layers are provided to demonstrate the method's effectiveness in real-world applications.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123940903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

I/O Scheduling for Limited-Size Burst-Buffers Deployed High Performance Computing 有限大小突发缓冲区的I/O调度部署高性能计算

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00021

Benbo Zha, Hong Shen

{"title":"I/O Scheduling for Limited-Size Burst-Buffers Deployed High Performance Computing","authors":"Benbo Zha, Hong Shen","doi":"10.1109/PDCAT46702.2019.00021","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00021","url":null,"abstract":"Burst-Buffers is a high throughput, small size intermediate storage system integrated between computing nodes and permanent storage system to mitigate the I/O bottleneck problem in modern High Performance Computing (HPC) platforms. This system, however, is unable to effectively handle variable-intensity I/O bursts resulted by unpredictable concurrent accesses to the shared Parallel File System (PFS). In this paper, we introduce a probabilistic I/O scheduling method that takes into account of the burst-buffer load state and instantaneous I/O load distribution of the system based on the probabilistic model of applications to relieve the I/O congestion when I/O load exceeds the PFS bandwidth caused by dynamic application interference. The proposed scheduling method for limited-size Burst-Buffers deployed HPC platforms makes online decision of probabilistic selection of concurrent I/O requests for going through (to PFS), buffering (to Burst-Buffers) or declination in accordance to both the available I/O bandwidth and the current buffer state in order to maximize system efficiency or minimize application dilation. Extensive experiment results on actual characteristic synthetic data show that our method handles the I/O congestion effectively.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114366970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Introducing PulseAT: A Tool for Analyzing System Utilization in Distributed Systems PulseAT:一个用于分析分布式系统中系统利用率的工具

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00057

Uwe Jahn, V. Poliakov, Meghadoot Gardi, Peter Schulz, Carsten Wolff

引用次数: 0

In the Quest of Trade-off between Job Parallelism and Throughput in Hadoop: A Stochastic Learning Approach for Parameter Tuning on the Fly 在Hadoop中寻求作业并行性和吞吐量之间的权衡:一种动态参数调优的随机学习方法

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2019-12-01 DOI: 10.1109/PDCAT46702.2019.00086

Ramesh Pokhrel, A. Rauniyar, A. Yazidi

{"title":"In the Quest of Trade-off between Job Parallelism and Throughput in Hadoop: A Stochastic Learning Approach for Parameter Tuning on the Fly","authors":"Ramesh Pokhrel, A. Rauniyar, A. Yazidi","doi":"10.1109/PDCAT46702.2019.00086","DOIUrl":"https://doi.org/10.1109/PDCAT46702.2019.00086","url":null,"abstract":"With the emergence of the concept of big data, Hadoop MapReduce has been the de facto standard programming model for processing a large amount of data stored on the different cluster nodes in a distributed manner. It is known that the implementation of MapReduce operation with the default configuration yields a low number of parallel running jobs. In fact, poor resource utilization and overall low performance are usually induced by the default configuration. Although a myriad of works has been carried out in the literature for optimally configuring Hadoop MapReduce, the absolute vast majority of those works only consider offline and static configuration. Those approaches are clearly ineffective as the load might change during execution requiring tuning again the configuration parameters. In this work, we rather focus on dynamical and adaptively configuring Hadoop MapReduce by changing the system level Maximum Application Master Resource in Percent (MARP) parameter on the fly. We show that adaptively tuning the MARP parameter yields a good trade-off between job parallelism and throughput. To achieve this, an optimal design which we call Adaptive Parameter Tuning of Hadoop (APTH) based on a novel variant of the Tsetlin Automata is devised. Comprehensive experimental results show that the resources are optimally and appropriately utilized, resulting in better job parallelism and throughput. Furthermore, it is found that our APTH approach spends 47% less time for job execution as compared to the default configuration.","PeriodicalId":166126,"journal":{"name":"2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"88 36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130795983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0